• Human Ecology Research
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• v.59 no.4
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• pp.543-556
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• 2021

This study aimed to investigate the factors that influence multicultural adolescents' proficiency in their mother's native language, or their immigrant mother's native language. A hierarchical regression analysis was performed on data from the survey answered by 1,028 multicultural adolescents aged 15 years old and whose mothers were from foreign countries for the 6th Multicultural Adolescents Panel Study(MAPS) conducted by the National Youth Policy Institute (NYPI) in 2016. The main results are as follows. First, multicultural adolescents' minority language proficiency was generally low and significant differences were observed according to their gender, parents' educational level, household income, and mother's native country. More specifically, a higher proficiency in minority language was found for girls than boys, adolescents with a higher parental educational level, adolescents with a higher income, and adolescents whose mothers were from Japan or China, compared with those from the Philippines, Thailand, or Vietnam. Second, a significant positive correlation was observed between multicultural adolescents' minority language proficiency and 1) foreign culture acceptance, 2) parent's educational support, and 3) the use of the minority language at home. Third, foreign culture acceptance, parents' educational support, and the use of the minority language at home were predictors of multicultural adolescents' minority language proficiency. The study is meaningful in that it examined multicultural adolescents' minority language proficiency, elucidating their bilingual development, whereas previous studies have only focused on their proficiency in Korean, which is the majority language.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.83-85
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• 2017

We conducted an unbiased near- to mid-infrared imaging and spectroscopic survey of the Large Magellanic Cloud (LMC) as a part of the AKARI Mission Program "Large-area Survey of the LMC" (LSLMC, PI: T. Onaka). An area of about 10 square degrees of the LMC was observed by five photometric bands (3.2, 7, 11, 15, and $24{\mu}m$) and a low-resolution slitless prism ($2-5{\mu}m$, R ~20) equipped with AKARI /IRC. We constructed and publicly released photometric and spectroscopic catalogues of point sources in the LMC based on the survey data. The catalogues provide a large number of near-infrared spectral data, coupled with complementary broadband photometric data. Combined use of the present AKARI LSLMC catalogues with other infrared point source catalogues of the LMC possesses scientific potential that can be applied to various astronomical studies.

• Structural Engineering and Mechanics
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• v.51 no.2
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• pp.249-265
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• 2014

The mechanical characteristics of materials are very essential in structural analysis for the accuracy of structural calculations. The estimation modulus of elasticity of concrete ($E_c$), one of the most important mechanical characteristics, is a very complex area in terms of analytical models. Many attempts have been made to model the modulus of elasticity through the use of experimental data. In this study, the neuro-fuzzy (NF) technique was investigated in estimating modulus of elasticity of concrete and a new simple NF model by implementing a different NF system approach was proposed. A large experimental database was used during the development stage. Then, NF model results were compared with various experimental data and results from several models available in related research literature. Several statistic measuring parameters were used to evaluate the performance of the NF model comparing to other models. Consequently, it has been observed that NF technique can be successfully used in estimating modulus of elasticity of concrete. It was also discovered that NF model results correlated strongly with experimental data and indicated more reliable outcomes in comparison to the other models.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.167-177
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• 2006

An important role of statistical analysis in science is interpreting observed data as evidence, that is 'what do the data say?'. Although standard statistical methods (hypothesis testing, estimation, confidence intervals) are routinely used for this purpose, the theory behind those methods contains no defined concept of evidence and no answer to the basic question 'when is it correct to say that a given body of data represent evidence supporting one statistical hypothesis against another?' (Royall, 1997). In this article, we use likelihood ratios to measure evidence provided by record values in favor of a hypothesis and against an alternative. This hypothesis is concerned on mean of an exponential model and prediction of future record values.

• Bulletin of the Korean Chemical Society
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• v.35 no.8
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• pp.2519-2522
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• 2014

Recently, the observation of the electrocatalytic behavior of individual nanoparticles (NPs) by electrochemical amplification method has been reported. For example, the Iridium oxide ($IrO_x$) NP collision on the Pt UME was observed via electrocatalytic water oxidation. However, the bare Pt UME had poor reproducibility for the observation of NP collision signal and required an inconvenient surface pre-treatment for the usage. In this manuscript, we has been investigated other metal electrode such as Cu UME for the reproducible data analysis and convenient use. The $IrO_x$ NP collision was successively observed on the bare Cu UME and the reproducibility in collision frequency was improved comparing with previous case using the $NaBH_4$ pre-treated Pt UME. Also, the adhesion coefficient between NP and the Cu UME was studied for better understanding of the single NP collision system.

• Proceedings of the KIEE Conference
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• 1996.07c
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• pp.1797-1800
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• 1996

In this study, the insulation selected in degra-dated-power cable has been observed and aging process about the insulation has been investigated. Most Insulation aging process initiates in the interface of semiconducting layer and the type of the process is the fractal form that was observed between the the semiconducting layer and insulation layer. It is possible to estimate degree of the degration of cable with breakdown test and obtained data have been used to get parameters in order to use Weibull distribution. With this method it is considered to be possible to estimate situation of degration and life prediction.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.